creators_name: He, Y. creators_name: Pérez Ipiña, J. M. creators_id: Yang-Hui.He.1@city.ac.uk type: article datestamp: 2023-01-10 11:57:01 lastmod: 2026-02-28 08:45:39 metadata_visibility: show title: Machine-learning the classification of spacetimes ispublished: pub subjects: QA subjects: QA75 subjects: QC full_text_status: public note: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) abstract: On the long-established classification problems in general relativity we take a novel perspective by adopting fruitful techniques from machine learning and modern data-science. In particular, we model Petrov's classification of spacetimes, and show that a feed-forward neural network can achieve high degree of success. We also show how data visualization techniques with dimensionality reduction can help analyze the underlying patterns in the structure of the different types of spacetimes. dates_date: 2022-05-26 dates_date: 2022-06-07 dates_date: 2022-09-10 dates_date_type: accepted dates_date_type: published_online dates_date_type: published publication: Physics Letters B volume: 832 publisher: Elsevier BV id_number: 10.1016/j.physletb.2022.137213 refereed: TRUE issn: 0370-2693 official_url: https://doi.org/10.1016/j.physletb.2022.137213 citation_doi: 10.1016/j.physletb.2022.137213 citation: He, Y. ORCID: 0000-0002-0787-8380 & Pérez Ipiña, J. M. (2022). Machine-learning the classification of spacetimes. Physics Letters B, 832, article number 137213. doi: 10.1016/j.physletb.2022.137213 document_url: https://openaccess.city.ac.uk/id/eprint/29549/1/1-s2.0-S0370269322003471-main.pdf